Probiotic effects on skin health: comprehensive visual analysis and perspectives
Bibliographic record
Abstract
Background: Bacteria play a crucial role in maintaining the health of human skin. Research has demonstrated that probiotics present notable benefits for extraintestinal organs. Despite the extensive research on the impact of probiotics on skin health, there is a notable absence of regulatory frameworks governing their external application, with no approval from the FDA for any probiotic products for external use. The aim of this study is to offer a thorough summary of the research status in the field since 2000 and project future trends. Method: The Web of Science Core Collection and SCI-Expanded index were selected for an extensive search of studies concerning the role of probiotics in skin health since 2000. A total of 1,306 publications were identified. Employing a double-blind method, two subsets of literature were scrutinized and subsequently combined for analysis. Using CiteSpace, this research explored key aspects such as primary countries, institutions, authors, journals, trending topics, research frontiers, and emerging patterns in research related to application of probiotic for skin health. Result: This article included 709 research papers. The number of published papers has shown a rapid increase. The United States had the highest number of research papers (128), and Canada had the highest intermediate centrality (0.23). The University of California System emerged as the most prolific institution. Huang, Chun-Ming has published the most articles, and his research is at the forefront among those prolific authors. Twelve clusters were identified, with cluster #0 skin microbiota, #3 mechanisms, and #8 antimicrobial being the most recent. As for the hot topic, "diversity," "health," "skin microbiome," "oxidative stress," "microbiota," and "antioxidants" have been at the forefront of the current field. The overall research trend has shifted from clinical trials to mechanistic exploration and from oral treatments to external applications, with the research level moving from general categories to specific strains. Conclusion: This paper summarized and visualized academic achievements in the field of probiotic application for skin health using CiteSpace and VOSviewer, offering a systematic and comprehensive perspective, along with a longitudinal overview of this research field.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.056 | 0.035 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".